THE INFLUENCE OF BDNF VAL66MET POLYMORPHISM ON COGNITION, DEPRESSION, QUALITY OF LIFE, AND MOTOR SYMPTOMS IN INDIVIDUALS WITH PARKINSON’S DISEASE AFTER DYNAMIC CYCLING
Bibliographic record
Abstract
BACKGROUND: Individuals with Parkinson's disease (PD) tend to have high inter-individual variability in symptoms and in response to exercise.A genetic variation called brain derived neurotrophic factor (BDNF) Val66Met polymorphism may influence variability of attention and executive function domains, depression symptoms, quality of life, and motor symptoms after dynamic cycling.PURPOSE: The first aim was to determine if the prevalence of the Val66Met polymorphism influenced to incidences of attention and executive dysfunction, depression symptoms, decreased quality of life, and motor symptoms.The second aim was to determine if Val66Met polymorphism influenced changes in attention and executive function, depression symptoms, quality of life, and motor symptoms after dynamic cycling.METHODS: Fourteen participants (n=10, 6M/4F Val-allele group, n=4, 2M/2F Met-allele group, 64±9 years old), diagnosed with PD performed WebNeuro® testing, Montreal Cognition Assessment (MoCA), Beck Depression Inventory (BDI-II), EuroQOL questionnaire, Kinesia ONETM assessment, and Val66Met polymorphism genotyping.The intervention involved three, 40 minute dynamic cycling sessions.RESULTS: There was no influence of Val66Met polymorphism on the pre-intervention scores of attention, executive function, MoCA (cognition), depression symptoms, or motor symptoms.There were also no influences of Val66Met polymorphism on changes in attention, executive function, MoCA, depression symptoms or motor symptom after dynamic cycling.However, Val66Met polymorphism did have a main effect of group for quality of life questionnaire regardless of the cycling intervention.DISCUSSION: This intervention did not promote overall improvements.However, there were some interesting trends in the data and a larger sample size will likely result in more conclusive results.I would like to thanks my parents, Don and Sharon, my boyfriend Nick, and all of my family and friends for their continued encouragement and support through this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".